Programming Animated Statistical Graphics with R

1. Learning Outcome

In this hands-on exercise, we will learn to

• create animated data visualization by using gganimate and plotly R packages

• reshape data using tidyr package

• process, wrangle and transform data using dplyr package

2. Getting Started

2.1 Installing and loading the required libraries

Firstly, let’s install and load the required packages:

• tidyverse: an opinionated collection of R packages designed for data import, data wrangling and data exploration

• plotly: to plot interative statistical graphs

• gganimate: to plot animated visualization

• gifski: to convert images to GIF animations

• gapminder: An excerpt of the data available at Gapminder.org. We’ll use its country_colors scheme.

• readxl: to read data from excel files

pacman::p_load(tidyverse, plotly, gganimate, gifski, gapminder, readxl)

2.2 Importing the data

In this hands-on exercise, we’ll use the Data worksheet from GlobalPopulation Excel workbook

Let’s start by importing the data.

col <- c("Country", "Continent")

globalPop <- read_xls("../../Data/GlobalPopulation.xls",
                      sheet = "Data") %>%
  mutate_each_(funs(factor(.)), col) %>% # to convert all character data into factor
  mutate(Year = as.integer(Year)) # to convert Year column into integer

Important

Note that mutate_each_() was deprecated in dplyr 0.7.0. and funs() was deprecated in dplyr 0.8.0. Therefore, we will replace the function with mutate_at() function. Alternatively, across() function can also be used to achieve the same output.

Example code using mutate_at() function.

col <- c("Country", "Continent")

globalPop <- read_xls("../../Data/GlobalPopulation.xls",
                      sheet = "Data") %>%
  mutate_at(col, as.factor) %>%
  mutate(Year = as.integer(Year))

Example code using across() function.

col <- c("Country", "Continent")

globalPop <- read_xls("../../Data/GlobalPopulation.xls",
                      sheet = "Data") %>%
  mutate(across(col, as.factor)) %>%
  mutate(Year = as.integer(Year))

3. Animated Data Visualisation: gganimate methods

Now, let’s use gganimate methods to create some animated visualizations. The visualizations are generated using the following functions:

• transition_(): defines how the data should be spread out and how it relates to itself across time.

• view_(): defines how the positional scales should change along the animation.

• shadow_(): defines how data from other points in time should be presented in the given point in time.

• enter_()/exit_(): defines how new data should appear and how old data should disappear during the course of the animation.

• ease_aes(): defines how different aesthetics should be eased during transitions.

3.1 Building a static population bubble plot

Let’s first create a static bubble plot using ggplot2 method.

ggplot(globalPop, aes(x = Old, 
                      y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

3.2 Building the animated bubble plot

Next, let’s make the bubbles move by using the two functions below:

• transition_time(): to create transition through distinct states in time (i.e. Year).

• ease_aes(): to control easing of aesthetics. The default is linear. Other methods are: quadratic, cubic, quartic, quintic, sine, circular, exponential, elastic, back, and bounce.

ggplot(globalPop, aes(x = Old, 
                      y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') +
  transition_time(Year) +       
  ease_aes('linear')          

4. Animated Data Visualisation: plotly

Now, let’s make the animated visualization interactive by using ggplotly() method.

4.1 Building an animated bubble plot: ggplotly() method

The plot

The code

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

Although we have turned off legend using show.legend = FALSE argument, it stills shows in the plot. To overcome this problem, theme(legend.position=‘none’) should be used as shown in the plot and code chunk below.

The plot

The code

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 frame = Year),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position = 'none')

ggplotly(gg)

4.2 Building an animated bubble plot: plot_ly() method

Another way to make animated bubble plot is to use plot_ly() function.

The plot

The code

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)

bp

This comes to the end of this hands-on exercise. I have learned many different methods to create animated visualizations in R. Hope you enjoyed it, too!

See you in the next hands-on exercise 🥰